Abstract: 【Objective】To study the application characteristics of deep learning and panoramic imaging technology in plant landscape index quantification and provide theoretical basis for the quantification of indexes.【Methods】Based on searches of the plant landscape evaluation in CNKI and Web of Science database,the application characteristics of traditional plant landscape high-frequency evaluation indexes were compiled. Classification was made according to whether it was suitable for applying deep learning and panoramic imaging technology index quantification. The application form,promotion role and reasons for operational challenges of the technology were analyzed.【Results】Deep learning and panoramic imaging technology optimized both the integrity of data collection and scientific of data processing in plant landscape index quantification. At the same time,this technology simplified complex manual operation processes,lowered technical expenses,and improved work efficiency,making it widely applicable in plant landscape index quantification.【Conclusion】In terms of the scope of application,deep learning was highly adaptable. In terms of applicable conditions,most of them were constrained by the limitations of image transmission information,which needed complementation by traditional research methods. In future development,there are still many operational evaluation indexes need to be practiced in plant landscape index quantification by deep learning and panoramic imaging technology.

Key words: Deep learning, Panoramic imaging technology, Plant landscape, Index quantification